35: Logistic Regression | TensorFlow | Core API | Tutorial

The video discusses in TensorFlow core API: Logistic regression for binary classification Dataset: Breast Cancer 00:00:00 - Overview 00:00:37 - Import libraries 00:02:27 - Set figure sizes 00:04:25 - Load dataset: Wisconsin Breast Cancer 00:10:36 - Check data: dataset.info(), .unique() 00:11:33 - Split dataset into train and test: .sample(), .drop() 00:13:19 - Separate features and target: x_train, y_train, x_test, y_test 00:16:25 - Preprocessing: .map() 00:19:54 - Preprocessing: tf.convert_to_tensor() 00:22:23 - Preprocessing: sns.pairplot() 00:24:57 - Descriptive statistics: .describe() 00:26:31 - Normalization: create class Normalize(tf.Module) 00:39:13 - * * * ERROR mentioned at 01:31:48 * * *: 00:29:07 - Normalization: normalize x_train, x_test 00:30:39 - Create and visualize sigmoid function 00:34:30 - Create Log loss function 00:35:50 - Gradient descent update rule: create class LogisticRegression(tf.Module) 00:39:13 - * * * ERROR mentioned at 01:13:39 * * *: self.built=True 00:41:13 - Check if class LogisticRegression returns values in range 0 to 1 00:44:32 - Create accuracy function 00:48:30 - Convert data: tf.data.Dataset.from_tensor_slices() 00:51:00 - Convert data: .shuffle().batch() 00:53:00 - Training: Set training parameters 01:05:59 - * * * ERROR * * *: test_losses.append(test_loss), test_accs.append(test_acc) 01:08:47 - Performance evaluation: plots for loss and accuracy 01:11:07 - * * * ERROR * * * (x * self.std) + self.mean 01:13:39 - * * * ERROR * * * self.built=True 01:17:00 - Performance evaluation: Confusion matrix 01:24:17 - Save model: create class ExportModule(tf.Module) 01:29:41 - Load saved model 01:31:24 - Ending notes # ---------------- # TensorFlow Guide # ---------------- https://www.tensorflow.org/guide/core/logistic_regression_core